Improving Radiology Report Conciseness and Structure via Local Large Language Models
Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute（机器学习系，H. Lee Moffitt癌症中心与研究所在读机构）；Deparmtent of Diagnostic Imaging & Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute（诊断影像与介入放射学系，H. Lee Moffitt癌症中心与研究所在读机构）；Department of Radiology, James A. Haley VA Hospital（放射学系，James A. Haley退伍军人医院）
Iryna Hartsock, Cyrillo Araujo, Les Folio, Ghulam Rasool
Radiology reports are often lengthy and unstructured, posing challenges for referring physicians to quickly identify critical imaging findings while increasing the risk of missed information. This retrospective study aimed to enhance radiology reports by making them concise and well-structured, with findings organized by relevant organs. To achieve this, we utilized private large language models (LLMs) deployed locally within our institution's firewall, ensuring data security and minimizing computational costs. Using a dataset of 814 radiology reports from seven board-certified body radiologists at Moffitt Cancer Center, we tested five prompting strategies within the LangChain framework. After evaluating several models, the Mixtral LLM demonstrated superior adherence to formatting requirements compared to alternatives like Llama. The optimal strategy involved condensing reports first and then applying structured formatting based on specific instructions, reducing verbosity while improving clarity. Across all radiologists and reports, the Mixtral LLM reduced redundant word counts by more than 53%. These findings highlight the potential of locally deployed, open-source LLMs to streamline radiology reporting. By generating concise, well-structured reports, these models enhance information retrieval and better meet the needs of referring physicians, ultimately improving clinical workflows.
Radiology-Llama2: Best-in-Class Large Language Model for Radiology
University of Georgia（佐治亚大学）；Harvard University（哈佛大学）；Second Xiangya Hospital（中南大学湘雅二医院）；Northwestern Polytechnical University（西北工业大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Mayo Clinic（梅奥诊所）；ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究中心）
CCS: Clinical Consensus Selection for Radiology Report Generation
School of Computing Science, University of Glasgow（格拉斯哥大学计算机科学学院）；School of Electrical and Computer Engineering, University of Sydney（悉尼大学电气与计算机工程学院）；Language Technology Lab, University of Cambridge（剑桥大学语言技术实验室）
RadAnnotate: Large Language Models for Efficient and Reliable Radiology Report Annotation
Department of Computer Science, University of California, Davis, CA, USA（加州大学戴维斯分校计算机科学系）；Department of Radiology, University of California, Davis, CA, USA（加州大学戴维斯分校放射学系）
ReXplain: Translating Radiology into Patient-Friendly Video Reports
Harvard University（哈佛大学）；Icahn School of Medicine, Mount Sinai（西奈山伊坎医学院）；Stanford University（斯坦福大学）；Los Angeles General Medical Center（洛杉矶综合医疗中心）；University of Maryland（马里兰大学）；SSM Health（SSM医疗）；Saint Louis University（圣路易斯大学）
Are Machine Rationales (Not) Useful to Humans? Measuring and Improving Human Utility of Free-Text Rationales
University of Southern California（南加州大学）；Tsinghua University（清华大学）；Meta AI；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；University of Washington（华盛顿大学）
The Era of Foundation Models in Medical Imaging is Approaching : A Scoping Review of the Clinical Value of Large-Scale Generative AI Applications in Radiology
Abn-BLIP: Abnormality-aligned Bootstrapping Language-Image Pre-training for Pulmonary Embolism Diagnosis and Report Generation from CTPA
Department of Diagnostic Imaging, Brown University Health（布朗大学健康中心诊断影像科）；Warren Alpert Medical School of Brown University（布朗大学沃伦·阿尔珀特医学院）；Department of Biomedical Engineering, Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院生物医学工程系）；Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院放射科）；Johns Hopkins University Division of Pulmonary and Critical Care Medicine（约翰霍普金斯大学肺科与重症医学科）；Department of Radiology, University of Colorado School of Medicine（科罗拉多大学医学院放射科）
BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks
Lehigh University（理海大学）；University of Georgia（佐治亚大学）；Samsung Research America（三星美国研究院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；University of Pennsylvania（宾夕法尼亚大学）；Children’s Hospital of Philadelphia（费城儿童医院）；University of Central Florida（中佛罗里达大学）；University of California, Santa Cruz（加州大学圣克鲁兹分校）；UTHealth Houston（休斯顿德克萨斯大学健康科学中心）；Mayo Clinic（妙佑医疗国际）；Penn Institute for Biomedical Informatics（宾夕法尼亚生物医学信息学研究所）；Leonard Davis Institute of Health Economics（伦纳德·戴维斯健康经济学研究所）；Stanford University School of Medicine（斯坦福大学医学院）；Stanford University（斯坦福大学）
Translating Inference-Time Control to Radiology Vision-Language Models: Activation Steering for Pneumonia Classification on Chest X-rays
Universidade Federal de São Paulo (UNIFESP)（圣保罗联邦大学）；Hospital Israelita Albert Einstein（以色列阿尔伯特·爱因斯坦医院）；Stanford University School of Medicine（斯坦福大学医学院）；DASA；University of Texas Southwestern Medical Center (UTSW)（德克萨斯大学西南医学中心）；Eden
ICON: Improving Inter-Report Consistency in Radiology Report Generation via Lesion-aware Mixup Augmentation
The Hong Kong Polytechnic University（香港理工大学）；Southern University of Science and Technology（南方科技大学）；Research Institute of Trustworthy Autonomous Systems（可信自主系统研究院）
Improving the Performance of Radiology Report De-identification with Large-Scale Training and Benchmarking Against Cloud Vendor Methods
Stanford University（斯坦福大学）；JP Morgan Chase & Co（摩根大通公司）；Sorbonne University（索邦大学）；University of Oxford（牛津大学）；NVIDIA（英伟达）；HOPPR；University of Pennsylvania（宾夕法尼亚大学）
The Path to Self-Evolving Clinical Systems: Scaling Medical Agents from Assistance to Autonomy
Hunan University（湖南大学）；ByteDance（字节跳动）；Duke University（杜克大学）；Westlake University（西湖大学）；The University of Hong Kong（香港大学）；Nanyang Technological University（南洋理工大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Macau（澳门大学）；The Ohio State University（俄亥俄州立大学）
Radiology Report Generation with a Learned Knowledge Base and Multi-modal Alignment
Institute of Computing Technology, CAS（中国科学院计算技术研究所）；University of Science and Technology of China（中国科学技术大学）；Tencent Medical AI Lab（腾讯医疗人工智能实验室）；University of Chinese Academy of Sciences（中国科学院大学）
A LongFormer-Based Framework for Accurate and Efficient Medical Text Summarization
Washington University in St. Louis（圣路易斯华盛顿大学）；Cornell University（康奈尔大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Northeastern University（东北大学）；The Chinese University of Hong Kong（香港中文大学）；Santa Clara University（圣克拉拉大学）
Evaluating Large Language Models for Automated Clinical Abstraction in Pulmonary Embolism Registries: Performance Across Model Sizes, Versions, and Parameters
Duke University Health System（杜克大学医疗系统）；Duke University（杜克大学）；Duke Clinical Research Institute（杜克临床研究所）
Prompt Triage: Structured Optimization Enhances Vision-Language Model Performance on Medical Imaging Benchmarks
Stanford University, Department of Computer Science, Stanford, CA, USA（斯坦福大学计算机科学系）；Stanford University, Department of Biomedical Data Science, Stanford, CA, USA（斯坦福大学生物医学数据科学系）；Stanford University, Stanford Institute for Human-Centered Artificial Intelligence, Stanford, CA, USA（斯坦福大学人类中心人工智能研究所）；Stanford University, School of Medicine, Department of Dermatology, Stanford, CA, USA（斯坦福大学医学院皮肤科系）；Stanford University, Department of Radiology, Stanford, CA, USA（斯坦福大学放射科）
